Reflections on Growing Economies and Fading Stimulus

2018 Global Investment Outlook

Machine Learning and Artificial Intelligence Underpin 2018 Tech ViewDec 1, 2017

2018 Outlook: “We remain positive on the information technology sector as enterprises seek to drive new revenue sources through digital transformation and become more productive with cloud and artificial intelligence technologies.”

We believe the information technology (IT) sector continues to have a solid growth outlook, with significant changes set to take place in the next few years. Technology is a far-reaching term these days, and it increasingly applies to non-IT sectors to varying degrees. Today’s progressively sophisticated technology and software have allowed companies to provide products and services that seemed impossible even 10 years ago. In particular, we believe the transition to technology-on-demand will be integrated in day-to-day life in the years ahead. This transition is being enabled by a large base of always-connected consumers, high-speed internet access and an expanding global cloud infrastructure.

Many investors have also been asking why the IT equity sector, in broad terms, has been so strong a performer during 2017 thus far. Part of the answer lies in our firm belief that a wider array of industries which were previously quite distant from pure technology are responding to an opportunity for—and a threat to—their core businesses brought about by hyper-connected consumers who are able to engage with companies and each other.

Artificial Intelligence and Machine Learning Set to Ramp up Significantly in 2018

Advancements over the past few years in artificial intelligence (AI) and machine learning have created opportunities for technology vendors in the data processing supply chain and for enterprises with unique and compounding datasets. We believe throughout much of 2016 and 2017 investors came to appreciate the growth opportunities that AI is creating for semiconductor companies, select high-performance data storage vendors, data integration vendors and cloud companies on the data-processing supply side. That said, we think over the course of 2018 and 2019 investors could come to appreciate how technology companies with large and compounding datasets will be able to combine their data with machine learning algorithms to create new sources of revenue, reduce costs, build predictive models and create compounding competitive advantages. We expect the entire data chain (data storage, processing and analysis) ultimately to benefit from this seismic shift, especially for companies that have unique datasets.

With their massive datasets, control of computing power and large teams of AI specialists, tech bellwethers in e-commerce and social networking are obvious beneficiaries of recent AI advancements. Less obvious, we believe, are the opportunities emerging for enterprise software-as-a-service (SaaS) application companies as machine learning advances and as customers embrace SaaS deployment models over more cumbersome “on-premise” technology deployments (meaning those installed in an enterprise’s data center).

We believe SaaS companies will benefit from AI technology because they control two unique and compounding datasets:

  • Product Usage Data. Unlike their legacy on-premise peers, SaaS companies have near-perfect visibility into how their products are being used. This usage data can be exploited with machine learning to improve a SaaS company’s products. We believe this should support SaaS companies’ pricing, reduce churn and make the sales process more efficient.
  • Customer Data. Unlike their legacy on-premise peers, SaaS companies have their customers’ data. This data can be mined to generate new revenue sources and keep customers more engaged with SaaS providers’ offerings. We believe this represents a profound change and can create significant opportunities for SaaS vendors well beyond the traditional software market.

The software firm’s recent acquisition of a 530 million-user professional social network makes things even more interesting, particularly when the data of both is combined. The professional social network contains three important datasets. First, it provides a map of who knows who between enterprises and within an enterprise. Second, title (CFO, VP of Procurement, etc.) data can be mined to infer an organization’s hierarchy and which employees have buying power. These first two datasets, especially when combined with email collaboration, chat and contact lists from the software firm’s subscription service, could prove valuable to sales people who are eager to find who might have purchasing authority and purchasing influence. That could ultimately facilitate the firm becoming much bigger in the large and fast-growing customer relationship management market. Third, the career networking company has curated resumes for 530 million users, with relevant work history, skills data and education data. This data can prove valuable, especially when combined with employee engagement data that has been gleaned from the subscription software, when building human resources applications for compensation, retention and even learning.


Michael Hasenstab, Ph.D.

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